In this demo-driven session, we will introduce the Probo package for teaching Python programming and concepts from computational finance to beginning programmers in the domain of finance. We'll show how Python is the perfect tool for teaching computational thinking to develop deeper quantitative reasoning. Jupyter notebooks, together with Python packages such as NumPy and Pandas, provide the ideal learning environment.
We will start by introducing the Probo package for derivative pricing and hedging. We will demo the pricing of European and American options via the famous Black-Scholes option pricing model. Other examples include Monte Carlo simulation and binomial trees. Using Probo, the answers to derivative pricing problems are right at the students' fingertips. Students can operationalize their understanding by going directly from the mathematics of derivative pricing theories to their implementation in clean and simple code.
We will end with a demonstration using Probo to teach the concept of dynamic hedging. Dynamic hedging is perhaps the crucial concept in modern financial derivatives theory. It is also one of the most difficult concepts to grasp. We'll show how developing deeper intuition is possible with computational thinking via Monte Carlo simulation of delta-hedging. By leveraging the power and simplicity of Python and Jupyter notebooks, the Probo package provides the ideal learning platform for students of computational finance.